提出定桨距变速风力发电机组的串级控制。外回路根据机组的运动方程采用自适应最优模糊控制,给出发电机电磁转矩设定值,实现参考转速的渐进跟踪。算法综合考虑风力发电机组的机械特性和电气特性,系统辨识作为控制算法的一部分自动执行...提出定桨距变速风力发电机组的串级控制。外回路根据机组的运动方程采用自适应最优模糊控制,给出发电机电磁转矩设定值,实现参考转速的渐进跟踪。算法综合考虑风力发电机组的机械特性和电气特性,系统辨识作为控制算法的一部分自动执行。内回路基于双馈发电机的定子磁场定向矢量控制系统模型,结合动态线性化和反馈稳态解耦技术,提出发电机有功、无功功率的预测函数控制。采用 PI 控制与本文的控制策略作对比仿真,结果表明提出方法的有效性。展开更多
摘要：针对风力发电机空气动力学和结构分析的要求，提出一种新的向量自回归（vectorautoregressive，VAR）Z维阵风速场仿真方法。在常规自回归（autoregressive，AR）法建模的基础上，根据维纳一辛钦公式，由协方差向量和功率谱求出自...摘要：针对风力发电机空气动力学和结构分析的要求，提出一种新的向量自回归（vectorautoregressive，VAR）Z维阵风速场仿真方法。在常规自回归（autoregressive，AR）法建模的基础上，根据维纳一辛钦公式，由协方差向量和功率谱求出自回归系数向量。其中输入参数为单点Davenport阵风功率谱（power special density,PSD）和互相关函数。基于此，推导出多维风速时程模型。算例采用一个3桨叶风力发电机所在风场，其中心高为H=30m，风力机转子半径肚11．6m，沿风力机叶尖扫过圆周均布12个点，取其中3点进行仿真，并采用Burg算法进行功率谱估计。采样频率0加．9Hz，频率采用点数Ar=1800，时间间隔0．1S。仿真结果表明，适当选取采样频率点数与时间间隔，可以在保证模拟功率谱计算精度的同时，具有快速高效的特点，弥补了传统方法在模拟三维风速时耗时长、精度低的缺点。展开更多
A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector...A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.展开更多
文摘提出定桨距变速风力发电机组的串级控制。外回路根据机组的运动方程采用自适应最优模糊控制,给出发电机电磁转矩设定值,实现参考转速的渐进跟踪。算法综合考虑风力发电机组的机械特性和电气特性,系统辨识作为控制算法的一部分自动执行。内回路基于双馈发电机的定子磁场定向矢量控制系统模型,结合动态线性化和反馈稳态解耦技术,提出发电机有功、无功功率的预测函数控制。采用 PI 控制与本文的控制策略作对比仿真,结果表明提出方法的有效性。
文摘摘要：针对风力发电机空气动力学和结构分析的要求，提出一种新的向量自回归（vectorautoregressive，VAR）Z维阵风速场仿真方法。在常规自回归（autoregressive，AR）法建模的基础上，根据维纳一辛钦公式，由协方差向量和功率谱求出自回归系数向量。其中输入参数为单点Davenport阵风功率谱（power special density,PSD）和互相关函数。基于此，推导出多维风速时程模型。算例采用一个3桨叶风力发电机所在风场，其中心高为H=30m，风力机转子半径肚11．6m，沿风力机叶尖扫过圆周均布12个点，取其中3点进行仿真，并采用Burg算法进行功率谱估计。采样频率0加．9Hz，频率采用点数Ar=1800，时间间隔0．1S。仿真结果表明，适当选取采样频率点数与时间间隔，可以在保证模拟功率谱计算精度的同时，具有快速高效的特点，弥补了传统方法在模拟三维风速时耗时长、精度低的缺点。
基金This work is supported by National Natural Science Foundation of China(No.51007047,No.51077087)Shandong Provincial Natural Science Foundation of China(No.20100131120039)National High Technology Research and Development Program of China(863 Program)(No.2011AA05A101).
文摘A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.
